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Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

(This article belongs to the Special Issue Computer and Mathematical Simulation of Microbial Fermentations: The Choice of Models, Parameter Estimation and Applications)

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15 pages, 708 KiB

Open AccessArticle

Mass Spectrometric Fingerprinting to Detect Fraud and Herbal Adulteration in Plant Food Supplements

by Surbhi Ranjan, Tanika Van Mulders, Koen De Cremer, Erwin Adams and Eric Deconinck

Molecules 2025, 30(14), 3001; https://doi.org/10.3390/molecules30143001 (registering DOI) - 17 Jul 2025

Abstract

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

(This article belongs to the Special Issue Analytical Chemistry in Europe: Towards Sustainability and Quality of Life, 2nd Edition)

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23 pages, 2859 KiB

Open AccessArticle

Air Quality Prediction Using Neural Networks with Improved Particle Swarm Optimization

by Juxiang Zhu, Zhaoliang Zhang, Wei Gu, Chen Zhang, Jinghua Xu and Peng Li

Atmosphere 2025, 16(7), 870; https://doi.org/10.3390/atmos16070870 (registering DOI) - 17 Jul 2025

Abstract

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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19 pages, 5415 KiB

Open AccessArticle

Intelligent Optimized Diagnosis for Hydropower Units Based on CEEMDAN Combined with RCMFDE and ISMA-CNN-GRU-Attention

by Wenting Zhang, Huajun Meng, Ruoxi Wang and Ping Wang

Water 2025, 17(14), 2125; https://doi.org/10.3390/w17142125 (registering DOI) - 17 Jul 2025

Abstract

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

(This article belongs to the Special Issue Optimization-Simulation Modeling of Sustainable Water Resource)

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23 pages, 418 KiB

Open AccessArticle

Do Economic Growth Targets Aggravate Environmental Pollution? Evidence from China

by Jianbao Chen and Chenwei Wu

Sustainability 2025, 17(14), 6534; https://doi.org/10.3390/su17146534 (registering DOI) - 17 Jul 2025

Abstract

How to balance the relationship between economic development and environmental protection is a common challenge faced by developing countries. Based on panel data from 30 Chinese provinces between 2008 to 2021, we analyze the impact of economic growth targets (EGTs) on environmental pollution (EP) using a spatial autoregressive threshold panel (SARTP) model. The empirical findings are as follows. (1) A 1% increase in the EP index in adjacent provinces leads to a 0.5870% increase in the observing province. (2) For provinces with EGTs above 7.5%, a 1% increase in the EGT results in a 0.3799% increase in the EP index. Conversely, its impact on EP is not significant. (3) As EGTs increase, the EP effect intensifies in central provinces, weakens in western provinces, and remains insignificant in eastern provinces; the EP effect of EGTs is significantly greater in provinces with a large population size and a low proportion of tertiary industry. (4) When the provincial EGT exceeds the central target by 0.5%, a 1% increase in the EGT results in a 0.4469% increase in the EP index. Our paper offers theoretical and empirical insights for alleviating EP and promoting sustainable economic development. Full article

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20 pages, 4974 KiB

Open AccessArticle

A Novel Shape Memory Alloy Actuated Bearing Active Preload System (SMA-BAPS) for Space Spindles

by Yuhang Zhang, Jun Jiang, Qiang Zhang, Yuanzi Zhou, Xiaoyong Zhang and Ruijie Sun

Aerospace 2025, 12(7), 637; https://doi.org/10.3390/aerospace12070637 (registering DOI) - 17 Jul 2025

Abstract

In this study, a novel shape memory alloy actuated bearing active preload system (SMA-BAPS) was proposed and experimentally demonstrated. SMA actuators placed in a single or antagonistic configuration were employed to drive the screw pair and thus fulfill one-way or bidirectional preload adjustment. Moreover, the self-locking screw pair was used to maintain the bearing preload without external energy input. To determine the parameters of screw pair and SMA actuators, a detailed design process was conducted based on analytical models of the proposed system. Finally, a screw pair with a lead of 3 mm and SMA actuators with a diameter of 0.5 mm and a length of 130 mm were adopted. Prototype tests were conducted to validate and evaluate the performance of the preload adjustment with the SMA-BAPS. The resistive torque and the natural frequency of spindles were recorded to represent the preload level of the bearing. Through the performance tests, the SMA-BAPS induced a maximum 47% variation in the resistive torque and a 20% variation in the spindle’s natural frequency. The response time of the SMA-BAPS was less than 5 s when the heating current of 5 A was applied on the SMA actuator. This design highlighted the compact size, quick response, as well as the bidirectional preload adjustment, representing its potential use in aerospace mechanisms and advanced motors. Full article

(This article belongs to the Section Astronautics & Space Science)

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17 pages, 449 KiB

Open AccessArticle

Immunotoxicity Studies on the Insecticide 2-((1-(4-Phenoxyphenoxy)propan-2-yl)oxy)pyridine (MPEP) in Hsd:Harlan Sprague Dawley SD^® Rats

by Victor J. Johnson, Stefanie C. M. Burleson, Michael I. Luster, Gary R. Burleson, Barry McIntyre, Veronica G. Robinson, Reshan A. Fernando, James Blake, Donna Browning, Stephen Cooper, Shawn Harris and Dori R. Germolec

Toxics 2025, 13(7), 600; https://doi.org/10.3390/toxics13070600 (registering DOI) - 17 Jul 2025

Abstract

The broad-spectrum insect growth regulator (IGR) and insecticide 2-((1-(4-Phenoxyphenoxy)propan-2-yl)oxy)pyridine (MPEP; also known as pyriproxyfen) is increasingly being used to address public health programs for vector control, initiated by the spread of Zika virus in 2015–2016. While considered relatively safe for humans under normal conditions, limited toxicology data are available. Current studies were undertaken to address the data gap regarding potential immunotoxicity of MPEP, with particular emphasis on host resistance to viral infection. Hsd:Harlan Sprague Dawley SD^® rats were treated for 28 days by oral gavage with doses of 0, 62.5, 125, 250 or 500 mg/kg/day of MPEP in corn oil. There was a dose-dependent increase in liver weights which is consistent with the liver playing a dominant role in MPEP metabolism. However, no histological correlates were observed. Following treatment, rats were subjected to a battery of immune tests as well as an established rat model of influenza virus infection to provide a comprehensive assessment of immune function and host resistance. While several of the immune tests showed minor exposure-related changes, evidenced by negative dose–response trends, most did not show significant differences in any of the MPEP treatment groups relative to vehicle control. Most notable was a negative trend in pulmonary mononuclear cell phagocytosis with increases in dose of MPEP. There was also a positive trend in early humoral immune response (5 days after immunization) to keyhole limpet hemocyanin (KLH) as evidenced by increased serum anti-KLH IgM antibodies which was followed later (14 days following immunization) by decreasing trends in anti-KLH IgM and IgG antibody levels. However, MPEP treatment had no effect on the ability of rats to clear the influenza virus nor the T-dependent IgM and IgG antibody response to the virus. The lack of effects of MPEP on host resistance to influenza suggests the immune effects were minimal and unlikely to present a hazard with respect to susceptibility to respiratory viral infection. Full article

(This article belongs to the Special Issue Environmental Contaminants and Human Health—2nd Edition)

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15 pages, 13730 KiB

Open AccessArticle

IGFBP5 Promotes Atherosclerosis in APOE^−/− Mice Through Phenotypic Transformation of VSMCs

by Aoqi Xiang, Hua Guan, Peihong Su, Lusha Zhang, Xiaochang Chen and Qi Yu

Curr. Issues Mol. Biol. 2025, 47(7), 555; https://doi.org/10.3390/cimb47070555 (registering DOI) - 17 Jul 2025

Abstract

Atherosclerosis constitutes a pathological process underlying cardiovascular diseases. There is growing evidence that IGFBP5 is a causative factor, although the conclusions of different studies are inconsistent. The present study aims to confirm the role and mechanism of IGFBP5 in atherosclerosis. The expression of IGFBP5 was induced in the skeletal muscle of male ApoE^−/− mice, an atherosclerosis model, using adeno-associated virus, resulting in elevated circulating IGFBP5 levels. Changes in blood lipids were detected, and pathological changes in the aorta were observed. Analysis of IGFBP5 function using RNA sequencing and validation were performed in a mouse aortic smooth muscle cell line. The results demonstrated that IGFBP5 overexpression exacerbated the development of aortic lesions in this murine models without any discernible alterations in lipid profile parameters; the arterial transcriptomic landscape revealed that heightened IGFBP5 levels predominantly influenced pathways governing smooth muscle cell proliferation and motility. In vitro experimentation corroborated these findings, showcasing the stimulatory effect of IGFBP5 on VSMC (vascular smooth muscle cell) proliferation and migration, provoking a transition toward a proliferative phenotype. IGFBP5 promotes atherosclerosis in ApoE^−/− mice through the phenotypic transformation of VSMCs. This finding suggests that IGFBP5 has the potential to serve as an indicator of atherosclerosis diagnosis and a target for therapeutic interventions in the future. Full article

(This article belongs to the Special Issue Molecules at Play in Cardiovascular Diseases)

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Graphical abstract

20 pages, 4067 KiB

Open AccessArticle

Research and Application of Low-Velocity Nonlinear Seepage Model for Unconventional Mixed Tight Reservoir

by Li Ma, Cong Lu, Jianchun Guo, Bo Zeng and Shiqian Xu

Energies 2025, 18(14), 3789; https://doi.org/10.3390/en18143789 (registering DOI) - 17 Jul 2025

Abstract

Due to factors such as low porosity and permeability, thin sand body thickness, and strong interlayer heterogeneity, the fluid flow in the tight reservoir (beach-bar sandstone reservoir) exhibits obvious nonlinear seepage characteristics. Considering the time-varying physical parameters of different types of sand bodies, a nonlinear seepage coefficient is derived based on permeability and capillary force, and a low-velocity nonlinear seepage model for beach bar sand reservoirs is established. Based on core displacement experiments of different types of sand bodies, the low-velocity nonlinear seepage coefficient was fitted and numerical simulation of low-velocity nonlinear seepage in beach-bar sandstone reservoirs was carried out. The research results show that the displacement pressure and flow rate of low-permeability tight reservoirs exhibit a significant nonlinear relationship. The lower the permeability and the smaller the displacement pressure, the more significant the nonlinear seepage characteristics. Compared to the bar sand reservoir, the water injection pressure in the tight reservoir of the beach sand is higher. In the nonlinear seepage model, the bottom hole pressure of the water injection well increases by 10.56% compared to the linear model, indicating that water injection is more difficult in the beach sand reservoir. Compared to matrix type beach sand reservoirs, natural fractures can effectively reduce the impact of fluid nonlinear seepage characteristics on the injection and production process of beach sand reservoirs. Based on the nonlinear seepage characteristics, the beach-bar sandstone reservoir can be divided into four flow zones during the injection production process, including linear seepage zone, nonlinear seepage zone, non-flow zone affected by pressure, and non-flow zone not affected by pressure. The research results can effectively guide the development of beach-bar sandstone reservoirs, reduce the impact of low-speed nonlinear seepage, and enhance oil recovery. Full article

(This article belongs to the Section H1: Petroleum Engineering)

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14 pages, 1351 KiB

Open AccessArticle

Fine-Scale Environmental Heterogeneity Drives Intra- and Inter-Site Variation in Taraxacum officinale Flowering Phenology

by Myung-Hyun Kim and Young-Ju Oh

Plants 2025, 14(14), 2211; https://doi.org/10.3390/plants14142211 (registering DOI) - 17 Jul 2025

Abstract

Understanding how flowering phenology varies across spatial scales is essential for assessing plant responses to environmental heterogeneity under climate change. In this study, we investigated the flowering phenology of the plant species Taraxacum officinale across five sites in an agricultural region of Wanju, Republic of Korea. Each site contained five 1 m × 1 m quadrats, where the number of flowering heads was recorded at 1- to 2-day intervals during the spring flowering period (February to May). We applied the nlstimedist package in R to model flowering distributions and to estimate key phenological metrics including flowering onset (5%), peak (50%), and end (95%). The results revealed substantial variation in flowering timing and duration at both the intra-site (quadrat-level) and inter-site (site-level) scales. Across all sites, the mean onset, peak, end, and duration of flowering were day of year (DOY) 89.6, 101.5, 117.6, and 28.0, respectively. Although flowering onset showed relatively small variation across sites (DOY 88 to 92), flowering peak (DOY 97 to 108) and end dates (DOY 105 to 128) exhibited larger differences at the site level. Sites with dry soils and regularly mowed Zoysia japonica vegetation with minimal understory exhibited shorter flowering durations, while those with moist soils, complex microtopography, and diverse slope orientations showed delayed and prolonged flowering. These findings suggest that microhabitat variability—including landform type, slope direction, soil water content, and soil temperature—plays a key role in shaping local flowering dynamics. Recognizing this fine-scale heterogeneity is essential for improving phenological models and informing site-specific climate adaptation strategies. Full article

(This article belongs to the Section Plant Ecology)

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41 pages, 2762 KiB

Open AccessReview

Memristor Emulator Circuits: Recent Advances in Design Methodologies, Healthcare Applications, and Future Prospects

by Amel Neifar, Imen Barraj, Hassen Mestiri and Mohamed Masmoudi

Micromachines 2025, 16(7), 818; https://doi.org/10.3390/mi16070818 (registering DOI) - 17 Jul 2025

Abstract

Memristors, as the fourth fundamental circuit element, have attracted significant interest for their potential in analog signal processing, computing, and memory storage technologies. However, physical memristor implementations still face challenges in reproducibility, scalability, and integration with standard CMOS processes. Memristor emulator circuits, implemented using analog, digital, and mixed components, have emerged as practical alternatives, offering tunability, cost effectiveness, and compatibility with existing fabrication technologies for research and prototyping. This review paper provides a comprehensive analysis of recent advancements in memristor emulator design methodologies, including active and passive analog circuits, digital implementations, and hybrid approaches. A critical evaluation of these emulation techniques is conducted based on several performance metrics, including maximum operational frequency range, power consumption, and circuit topology. Additional parameters are also taken into account to ensure a comprehensive assessment. Furthermore, the paper examines promising healthcare applications of memristor and memristor emulators, focusing on their integration into biomedical systems. Finally, key challenges and promising directions for future research in memristor emulator development are outlined. Overall, the research presented highlights the promising future of memristor emulator technology in bridging the gap between theoretical memristor models and practical circuit implementations. Full article

(This article belongs to the Section E：Engineering and Technology)

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13 pages, 1471 KiB

Open AccessArticle

Effect of X-Ray Tube Angulations and Digital Sensor Alignments on Profile Angle Distortion of CAD-CAM Abutments: A Pilot Radiographic Study

by Chang-Hun Choi, Seungwon Back and Sunjai Kim

Bioengineering 2025, 12(7), 772; https://doi.org/10.3390/bioengineering12070772 (registering DOI) - 17 Jul 2025

Abstract

Purpose: This pilot study aimed to evaluate how deviations in X-ray tube head angulation and digital sensor alignment affect the radiographic measurement of the profile angle in CAD-CAM abutments. Materials and Methods: A mandibular model was used with five implant positions (central, buccal, and lingual offsets). Custom CAD-CAM abutments were designed with identical bucco-lingual direction contours and varying mesio-distal asymmetry for the corresponding implant positions. Periapical radiographs were acquired under controlled conditions by systematically varying vertical tube angulation, horizontal tube angulation, and horizontal sensor rotation from 0° to 20° in 5° increments for each parameter. Profile angles, interthread distances, and proximal overlaps were measured and compared with baseline STL data. Results: Profile angle measurements were significantly affected by both X-ray tube and sensor deviations. Horizontal tube angulation produced the greatest profile angle distortion, particularly in buccally positioned implants. Vertical x-ray tube angulations beyond 15° led to progressive underestimation of profile angles, while horizontal tube head rotation introduced asymmetric mesial–distal variation. Sensor rotation also caused marked interthread elongation, in some cases exceeding 100%, despite vertical projection being maintained. Profile angle deviations greater than 5° occurred in multiple conditions. Conclusions: X-ray tube angulation and sensor alignment influence the reliability of profile angle measurements. Radiographs with > 10% interthread elongation or crown overlap may be inaccurate and warrant re-acquisition. Special attention is needed when imaging buccally positioned implants. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

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33 pages, 617 KiB

Open AccessArticle

Discourse of Military-Assisted Urban Regeneration in Colombo: Political and Elite Influences on Displacing Underserved Communities in Postwar Sri Lanka

by Janak Ranaweera, Sandeep Agrawal and Rob Shields

Real Estate 2025, 2(3), 11; https://doi.org/10.3390/realestate2030011 (registering DOI) - 17 Jul 2025

Abstract

This study examines the political and elite motives behind Colombo’s ‘world-class city’ initiative and its impact on public housing in underserved communities. Informed by interviews with high-ranking government officials, including urban planning experts and military officers, this study examines how President Rajapaksa’s elite-driven postwar Sri Lankan government leveraged military capacities within the neoliberal developmental framework to transform Colombo’s urban space for political and economic goals, often at the expense of marginalized communities. Applying a contextual discourse analysis model, which views discourse as a constellation of arguments within a specific context, we critically analyzed interview discussions to clarify the rationale behind the militarized approach to public housing while highlighting its contradictions, including the displacement of underserved communities and the ethical concerns associated with compulsory relocation. The findings suggest that Colombo’s postwar public housing program was utilized to consolidate authoritarian control and promote speculative urban transformation, treating public housing as a secondary aspect of broader political and economic agendas. Anchored in militarized urban governance, these elite-driven strategies failed to achieve their anticipated economic objectives and deepened socio-spatial inequalities, raising serious concerns about exclusionary and undemocratic planning practices. The paper recommends that future urban planning strike a balance between economic objectives and principles of spatial justice, inclusion, and participatory governance, promoting democratic and socially equitable urban development. Full article

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22 pages, 435 KiB

Open AccessArticle

Sustainable Entrepreneurship in Emerging Economies: The Role of Financial Planning, Environmental Consciousness, and Artificial Intelligence in Ecuador—A Cross-Sectional Study

by Martha Cecilia Aguirre Benalcázar, Marcia Fabiola Jaramillo Paredes and Oscar Mauricio Romero Hidalgo

Sustainability 2025, 17(14), 6533; https://doi.org/10.3390/su17146533 (registering DOI) - 17 Jul 2025

Abstract

This study investigates the interconnected roles of financial planning, environmental consciousness, and artificial intelligence (AI) in fostering sustainable entrepreneurship among merchants in Machala, Ecuador. Through structural equation modeling analysis of data from 300 entrepreneurs, we found that financial planning positively influences both sustainable entrepreneurship (β = 0.508, p < 0.001) and environmental consciousness (β = 0.421, p < 0.001). Environmental consciousness demonstrates a significant impact on sustainable business development (β = 0.504, p < 0.001), while AI integration emerges as a powerful enabler of both financial planning (β = 0.345, p < 0.001) and sustainable entrepreneurship (β = 0.664, p < 0.001). The findings reveal how AI technologies can democratize access to sophisticated sustainability planning tools in resource-constrained environments, potentially transforming how emerging market entrepreneurs approach environmental challenges. This research advances our understanding of sustainable entrepreneurship by demonstrating that successful environmental business practices in developing economies require an integrated approach combining financial literacy, ecological awareness, and technological adoption. The results suggest that policy interventions supporting sustainable entrepreneurship should simultaneously address financial capabilities, environmental education, and technological accessibility to maximize their impact on sustainable development. Full article

(This article belongs to the Special Issue AI-Driven Entrepreneurship and Sustainable Business Innovation)

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22 pages, 4581 KiB

Open AccessArticle

Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective

by Ibrahim Dogonyaro and Amira Elnokaly

Intell. Infrastruct. Constr. 2025, 1(2), 5; https://doi.org/10.3390/iic1020005 (registering DOI) - 17 Jul 2025

Abstract

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

(This article belongs to the Special Issue From Concept to Reality: Digital Innovations Driving the Future of Intelligent Infrastructure and Construction)

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